Determinants of Cardiovascular Complication Among Hypertensive Patient in Ethiopia: Systematic Review and Meta-Analysis 2025
Bibliographic record
Abstract
Background: The CVD mortality rate is particularly high in sub-Saharan Africa, estimated at around 350 per 100,000 population. This is higher than the global average, estimated at 286 per 100,000 population. People in sub-Saharan Africa who have poorly controlled hypertension are significantly more vulnerable to adverse cardiovascular disease events than people in other parts of the world. This study aims to identify the determinants of cardiovascular complications. Methods and Materials: Articles were retrieved from PubMed, Scopus, PsycINFO, and goggle scholar databases for this analysis. We assessed methodological quality using the Newcastle-Ottawa Scale. An inverse-variance-weighted random-effects model meta-analysis was performed to estimate the pooled odds ratio (OR) and its 95% confidence interval (CI) for determinants. The I2 test statistic was used to check between-study heterogeneity. A p-value of less than 0.05 used to declare Statical significance. Results: Six studies comprising of 2, cross-sectional studies, 3 cohort and 1 case-control studies with a good methodological quality included in this study. Most studies were conducted in Amhara region and published from 2019 onwards. Physical activity (OR: 3.07, 95% CI: 2.18-4.32), smoking history (OR: 6.76, 95% CI: 1.14-40.02), baseline cardiovascular complications (OR: 6.15, 95% CI: 3.89-9.74), and duration of hypertension (OR: 2.64, 95% CI: 1.85-3.77) were determinants of cardiovascular complication. Conclusion: According to this study physical activity, smoking history, baseline cardiovascular complication and duration of hypertension were determinants of cardiovascular complication. So, it is the important to promote regular physical activity, smoking cessation, and close monitoring of cardiovascular health in hypertensive patients to mitigate the risks of complications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.031 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".